import polars as pl import time # 读取 CSV 文件 start = time.time() df_pl_gpu = pl.read_csv('test_data.csv') load_time_pl_gpu = time.time() - start # 过滤操作 start = time.time() filtered_pl_gpu = df_pl_gpu.filter(pl.col('value1') > 50) filter_time_pl_gpu = time.t...
AI代码解释 math_score=df.set_index(['Gender','School'])['Math'].sort_index()grouped_score=df.set_index(['Gender','School']).sort_index().\groupby(lambda x:(x,'均分及格'ifmath_score[x].mean()>=60else'均分不及格'))forname,_ingrouped_score:print(name) d). groupby的[]操作 可...
dataframe.column_name.unique() # 这一行,会把一个series里面所有可能出现的值列出来。 用index或column来筛选: df_Tableau.filter(like = "Total", axis = 0) # axis=0表示对于index筛选,axis=1表示用column筛选 # 用来筛选的指标可以用item、like、regex来控制 # item表示必须完全匹配label # like表示labe...
py in apply(self, f, axes, filter, do_integrity_check, consolidate, **kwargs) 3089 3090 kwargs['mgr'] = self -> 3091 applied = getattr(b, f)(**kwargs) 3092 result_blocks = _extend_blocks(applied, result_blocks) 3093 /Users/Ted/anaconda/lib/python3.6/site-packages/pandas/core/...
.filter(pl.col("Category").is_in(["A","B"])) ) 如果表达式是 Eager 执行,则会多余地对整个 DataFrame 执行 groupby 运算,然后按 Category 筛选。 通过惰性执行,DataFrame 会先经过筛选,并仅对所需数据执行 groupby。 4)表达性 API 最后,Polars 拥有一个极具表达性的 API,基本上你想执行的任何运算都...
'filter', 'first', 'first_valid_index', 'floordiv', 'ge', 'get', 'groupby', 'gt', 'hasnans', 'head', 'hist', 'iat', 'idxmax', 'idxmin', 'iloc', 'index', 'infer_objects', 'interpolate', 'is_monotonic', 'is_monotonic_decreasing', 'is_monotonic_increasing', 'is_unique'...
insert() Insert a column in the DataFrame interpolate() Replaces not-a-number values with the interpolated method isin() Returns True if each elements in the DataFrame is in the specified value isna() Finds not-a-number values isnull() Finds NULL values items() Iterate over the columns of...
df.insert(loc= 1,column='Python3.8,value=2048) 1. 2. 3. 4. 5. 6. 第三节 数据的链接(join SQL风格) 数据集的合并(merge)或连接(join)运算是通过⼀个或者多个键将数据链接起来的。这些运算是关系型数据库的核⼼操作。pandas的merge函数是数据集进⾏join运算的主要切⼊点。
'filter','first','get_group','groups','head','hist','idxmax','idxmin','indices','last','mad','max','mean','median','min','ndim','ngroup','ngroups','nth','nunique','ohlc','pad','pct_change','pipe','plot','prod','quantile','rank','resample','rolling','sem','shift...
sort_values astype resample shape to_xarray to_period kurt ffill idxmax plot to_clipboard cumsum nlargest var add abs any tshift nunique count combine keys values set_axis isnull sparse first_valid_index combine_first ewm notnull empty mask truncate to_csv bool at clip radd to_markdown ...